IGE: STEM Graduate training in Data Science: solution-oriented, student-led, team-based, computationally-enriched (SOLSTICE) training

IGE：数据科学领域的 STEM 研究生培训：以解决方案为导向、学生主导、基于团队、计算丰富的 (SOLSTICE) 培训

• 批准号: 1855886
• 负责人: Elena Naumova
• 金额: $ 49.96万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1855886&HistoricalAwards=false
• 关键词: IGE; STEM; Graduate; training; Data

There has been a rapid increase in both the amount and type of data collected in many fields of science, technology and engineering; this requires new approaches to train graduate students to meet these needs. The new data revolution calls for a new data-savvy workforce capable of using data effectively to answer important scientific and practical questions and to communicate the results to a broad audience. These data scientists should have strong technical and leadership skills and the ability to work in teams comprising different and complementary expertise. To meet this training need, the National Science Foundation Innovations of Graduate Education (IGE) award to Tufts University will test an innovative approach to project-based learning, teaching graduate students in data-intensive fields using real datasets to solve real-world problems. This solution-oriented, student-led, team-based, computationally-enriched (SOLSTICE) training environment will offer multidisciplinary team role-play and experience in data analytics and scientific communication. This project will evaluate the effectiveness of the SOLSTICE approach in providing an adaptable method for instructors in a range of fields that use large datasets at diverse institution types. As the SOLSTICE approach is refined, it will pave the way for students to enter the workforce with strong technical skills in data analytics and be effective members of research teams. The project will answer two overarching research questions: (1) To what extent does the SOLSTICE approach provide opportunities for students to achieve 21st-century data intensive knowledge, skills, and attitudes? and (2) Are there variations in students' achievement over time, across different courses that use the SOLSTICE approach, and across different STEM disciplines? The approach consists of three components: (1) 3D Role Play, in which each student plays the role of Team Lead, Collaborator, and Reviewer in three different interdisciplinary teams aiming to develop, execute and communicate a research project based on authentic secondary data analyses; (2) Feedback on Feedback, which provides students with experience in giving and receiving effective critiques to their peers to strengthen inter-team communication; and (3) Data Analysis Roadmap, which offers knowledge-building guides and examples of key elements for efficient data analysis plans and other documents needed for effective communication of results. The approach will be tested and evaluated in six data-intensive courses in the Friedman School of Nutrition and in the School of Engineering, with approximately 270 participants monitored before, during, and for at least six months after participation in a SOLSTICE-based course. Educational resources will be developed based on successful components to guide replications of the training process, to train faculty in the SOLSTICE approach, and to disseminate it to other STEM fields. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

科学、技术和工程许多领域收集的数据数量和类型都在迅速增加；这需要新的方法来培训研究生以满足这些需求。 新的数据革命需要一支新的精通数据的劳动力队伍，能够有效地利用数据来回答重要的科学和实际问题，并将结果传达给广大受众。这些数据科学家应该具有强大的技术和领导技能，以及在由不同和互补的专业知识组成的团队中工作的能力。为了满足这一培训需求，塔夫茨大学获得的美国国家科学基金会研究生教育创新奖 (IGE) 将测试基于项目的学习的创新方法，在数据密集型领域教授研究生使用真实数据集来解决现实世界的问题。这种以解决方案为导向、以学生为主导、以团队为基础、计算丰富的 (SOLSTICE) 培训环境将提供多学科团队角色扮演以及数据分析和科学交流方面的经验。该项目将评估 SOLSTICE 方法的有效性，为在不同机构类型使用大型数据集的一系列领域的教师提供一种适应性强的方法。随着 SOLSTICE 方法的完善，它将为学生进入具有强大数据分析技术技能的劳动力队伍并成为研究团队的有效成员铺平道路。该项目将回答两个首要研究问题：(1) SOLSTICE 方法在多大程度上为学生提供获得 21 世纪数据密集型知识、技能和态度的机会？ (2) 随着时间的推移，使用 SOLSTICE 方法的不同课程以及不同的 STEM 学科，学生的成绩是否存在差异？该方法由三个组成部分组成：(1) 3D 角色扮演，其中每个学生在三个不同的跨学科团队中扮演团队领导、合作者和审阅者的角色，旨在开发、执行和交流基于真实二次数据分析的研究项目； （2）反馈中的反馈，为学生提供向同伴发出和接受有效批评的经验，以加强团队间的沟通； (3) 数据分析路线图，提供有效数据分析计划的知识构建指南和关键要素示例以及有效沟通结果所需的其他文件。该方法将在弗里德曼营养学院和工程学院的六门数据密集型课程中进行测试和评估，大约 270 名参与者在参加基于 SOLSTICE 的课程之前、期间和之后至少六个月受到监测。教育资源将基于成功的组件来开发，以指导培训过程的复制，对教师进行 SOLSTICE 方法的培训，并将其传播到其他 STEM 领域。研究生教育创新 (IGE) 计划专注于研究生教育研究。 IGE 的目标是试点、测试和验证研究生教育的创新方法，并产生将这些方法推向更广泛社区所需的知识。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。